Earn Money From Your Podcasts





Sell podcast ads online
Backtracks Ads Storefront is a pre-built and hosted advertising storefront for your ad inventory and that is optimized to attract and convert the right advertisers for your content, plus it’s enriched with our data verification and metrics. It’s a simple and easy to online checkout for your podcast and audio inventory in a dedicated storefront. We process payments, uploads, and workflows securely online. Our version of podcast advertising and revenue generation is meant to create an optimal experience for advertisers, publishers, and listeners. Ads are matched to podcasts based on user interest, meaning advertisers get more revenue, podcasters make their advertisers happier, and listeners find actual value from the products and services they hear about.

Deliver more value. Grow your revenue.
Prove the value of your audience to advertisers and sponsors with verified and direct measurement by Backtracks. Provide sharp looking automated reports and an advertiser portal. Show real-time mid-campaign data and post-campaign success automatically by using Backtracks’ podcast reporting and revenue management tools. Tell audio stories that mean business. With precise and actionable analytics, you’ll be able to make changes to campaigns on the fly and create a better advertising experience for all parties involved.

We don’t compete with you
Backtracks provides the technology and tools to help you run a successful podcast business. We complement and cooperate with you. We’re not the content publisher, we’re not a consumer listening app, and we’re not the advertiser. Our goal is to make everyone involved in the advertising process to have a more enriching and profitable experience. Backtracks is constantly improving and perfecting our platform for our users.
Deliver a superior experience
on every listen







We’re Flexible.
Publisher Reads or
Advertiser Provided Ads.
Provide your own audio or have an advertiser provide the ad audio. No matter where the content comes from we’ll optimize your revenue-generating podcast advertising campaigns and help you get the outcomes you deserve. Want to advertise your products and services on old episodes of podcasts? Need to replace an ad about a promotion that is no longer active? All of these are possible with our flexible platform. Whatever method of advertising works best in the podcast context is what you’ll be able to implement. Highly customizable advertising ensures you’re maximizing value.

Privacy First. We Don’t Track
Your Users.
Backtracks Podcast Monetization does not track or profile ANY of your users. It’s that simple. We believe that advertising can be optimized without being invasive. By using our advanced platform, we’re able to perfectly match publishers and creators. How do we do this? We use anonymized heuristics and AI to match advertisers and publishers so that an ad for a local lumber store to a woodworking podcast can find one another. No more cognitive dissonance in your advertising. Listeners will hear ads that appeal to them, podcasters will provide value to their sponsors, and sponsors increase their revenues. No privacy, GDPR, or CCPA issues here.

Podcast Monetization
at Scale
Backtracks is the platform for podcast revenue generation, monetization, and data at scale. We make sure the best ads and the best content find each other. We’re not the content publisher, we’re not a consumer listening app, and we’re not the advertiser. We add value for the listener, the advertisers, and publishers with our data and technology. Publishers, publisher-networks, developers, and agencies can use even Backtracks’ APIs and SDKs to programmatically interact with campaigns and run reports for campaigns that are large or small. You can scale your advertising operation as needed with Backtracks.
Pricing
Integrated.
AD REVENUE
Everything you need to manage, measure, and monetize podcasts
Integrated advertising, campaign reporting, storefront, and advertiser portal
No setup fees
Solutions
Volume discounts
Multi-product discounts
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